System and method for presenting actionable program performance information based on audience components

ABSTRACT

Systems and methods for presenting actionable program performance information correlated with content are disclosed. A method includes obtaining primary content information related to first content distributed to a plurality of viewers during a particular time duration; obtaining secondary content information related to the first content, wherein the secondary content information includes information identified based on the first content; obtaining activity information of the plurality of viewers of the first content during the particular time duration; determining a plurality of activity component information corresponding to a plurality of activity categories; storing the plurality of activity component information to be associated with the first content; and displaying data of at least one of the plurality of activity component information at a first time point along with the first content.

BACKGROUND

Collecting and utilizing information regarding media consumptionactivities and habits of audience members has long been an importantgoal for content distributors and creators, as well as advertisers.Having information on what programming content is being watched, as wellas information on who is watching them, is often used as a startingpoint for determining programming ratings, and in turn, the relativevalues of advertisement placement during one programming content overanother.

Collecting audience data included surveying a sample group comprised ofrandomly chosen viewers. Target audience members of the sample groupwould self-report their viewing history or habits over a determinedperiod of time. Audience data gathering systems evolved withtechnological advancements to include set meters which were installed inviewers' homes and connected to televisions or other media consumptiondevices. These set meters recorded and collected the viewing habits ofthe viewer and transmitted them to a central database and server wherethe information was collected and analyzed.

Other implementations have also been developed with the advent of newmedia delivery channels and the increase in data collectioncapabilities. Particularly, some existing viewer information systemsinclude the capability to analyze ratings information on increasinglygranular levels, including daily, hourly, quarter-hourly,minute-by-minute, and even second-by-second bases. These systems mayfurther include the ability to analyze audience gains including tune-in(turning on a television) and switch-in (changing from another channel)(collectively discussed herein as tune-in), and tune-out (audiencelosses, also referred to as retention) event information, which providesinformation regarding the time at which certain viewers tuned-in to, ortuned-out from, the particular programming.

The information provided by these known systems has been valuable inevaluating the success and appeal of certain programming content, aswell as determining values for advertising time related to the programs.These and other existing data collection and analysis systems however,have been limited to providing only fundamental information on whatprogramming content is being consumed, when it is being consumed, and insome advanced cases, who is watching. It has also failed to removerandom movement from the analyzed data, where viewers who tune-in to aprogram purely by chance—without knowing what program they are tuning-into—are analyzed in the same manner as another viewer who intentionallytuned-in to the program based on knowledge of the content.

For example, FIG. 1 shows a chart 100 of program ratings informationprovided for a particular content provider for a period of one month inSeptember, according to an audience measurement system of the relatedart. This extremely broad view of the data shows only the average netimpressions, or the average number of viewers that watched a givenprogram broadcast on a given day. While this type of information may beuseful in charting the overall numbers of viewers who at one point oranother was exposed to each particular program, it lacks any informationas to specifics of viewing patterns, viewing habits, or context to thenumbers with respect to the actual content being presented during eachprogram. Where two programs may be similar in content or presentationtime, this lack of detailed information may make it difficult forcontent providers and creators to understand the reasons behinddisparities in audience data from one program to another.

FIG. 2 shows a more detailed graph 200 providing a view of audience dataduring the presentation of a particular Program 1 discussed in FIG. 1according to a more granular view of the same audience measurementsystem of the related art. This view graphs the number of netimpressions throughout the duration of the presentation at one minuteincrements, including descriptions of various distinct segments of theprogram itself. For example, in the case of a talk show, Program 1 maybe comprised of 6 content based segments, where each segment isidentified by a guest of the talk show or a particular topic discussedduring the talk show. Therefore in addition to a line 207 charting theprogression of net impressions throughout the duration of the program,the graph 200 may include descriptors 201-206 of each segment, providinga visual correlation between a change in the net impressions and thecorresponding segment presented at the time of the change.

While this type of detailed view shown in FIG. 2 provides much moreinformation for content distributors for understanding the contextbehind a program's rating numbers, the graph of FIG. 2 still fails topresent sufficient granularity for content distributors—and inparticular, content creators—as to the true context driving the viewingnumbers. Measurement of changes in net impression may also not take intocontext various factors for significant changes, such as lead-in(viewership due to a program that immediately precedes another program),seasonality (viewership attributable the day of the week and month ofthe year), and non-content related tune-out (drop in viewershipattributable to a non-content related factor), thus creating skewed andunreliable data. For content distributors, and particularly for contentcreators, the lack of context provided by existing audience measurementsystems, even when charted on a minute-by-minute basis, reduces theinformation's value and prevents confident action in distributing orcreating content with the purpose of improving the content andgenerating high viewership ratings.

Thus, the existing systems and methods have been unable to providemeaningful information as to the context surrounding the audience dataprovided, and therefore the existing systems have thus far been limitedin value to entities such as content creators or content providers forgaining a detailed and reliable understanding of viewer behavior.Therefore, it may be advantageous for a system to present actionableprogram performance information which includes data related to acorrelation between the content being consumed and components of theaudience data collected.

SUMMARY

Embodiments of the present disclosure include a system and method forpresenting actionable program performance information within the contextof corresponding content. An embodiment of a method according to thepresent disclosure may include obtaining primary content informationrelated to first content, wherein the first content is distributed to aplurality of viewers during a particular time duration and the primarycontent information comprises audio and video of the first content,obtaining secondary content information related to the first content,wherein the secondary content information comprises informationidentified based on the first content, obtaining activity information ofthe plurality of viewers of the first content during the particular timeduration, determining a plurality of activity component informationcorresponding to a plurality of activity categories based on the primarycontent information and the secondary content information, storing theplurality of activity component information to be associated with thefirst content, and displaying data of at least one of the plurality ofactivity component information at a first time point within theparticular time duration along with the first content at the first timepoint.

Another embodiment of a method according to the present disclosure mayinclude displaying a reproduction of first video content with firstactivity information for a presentation duration of the first videocontent, displaying a reproduction of second video content with secondactivity information for a presentation duration of the second videocontent, wherein the first activity information corresponds to agraphical representation of audience retention during presentation ofthe first content and the second activity information corresponds to agraphical representation of audience retention during presentation ofthe second content. The first content and the second content may bedisplayed simultaneously.

Other embodiments of the present disclosure may include a systemincluding a memory configured to store information, a receiverconfigured to receive information, and one or more controllers. The oneor more controllers may be configured to receive primary contentinformation related to first content via the receiver, wherein the firstcontent is distributed to a plurality of viewers during a particulartime duration and the primary content information comprises audio andvideo of the first content, receive secondary content informationrelated to the first content via the receiver, wherein the secondarycontent information comprises information identified based on the firstcontent, receive activity information of the plurality of viewers of thefirst content during the particular time duration via the receiver,determine a plurality of activity component information corresponding toa plurality of activity categories based on the primary contentinformation and the secondary content information, cause the memory tostore the plurality of activity component information to be associatedwith the first content, and output data of at least one of the pluralityof activity component information at a first time point within theparticular time duration along with the first content at the first timepoint. The system of some embodiments may include a display, and the oneor more controllers may be configured to output the data via thedisplay.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects and features of the present disclosure willbecome more apparent upon consideration of the following description ofembodiments, taken in conjunction with the accompanying drawing figures.

FIG. 1 is an illustration of program ratings information provided for aparticular content provider, according to an audience measurement systemof the related art.

FIG. 2 is an illustration of audience data during the presentation of aparticular program according to the audience measurement system of FIG.1.

FIG. 3 is an illustration of a system for presenting actionable programperformance information according to an embodiment of the presentdisclosure.

FIG. 4 is an illustration of an aspect of the system for presentingactionable program performance information according to an embodiment ofthe present disclosure.

FIGS. 5A and 5B are illustrations showing identified segments ofprogramming content according to embodiments of the present disclosure.

FIG. 6 is an illustration of another aspect of the system for presentingactionable program performance information according to an embodiment ofthe present disclosure.

FIG. 7 is an illustration of an interface providing actionable programperformance information according to an embodiment of the presentdisclosure.

FIG. 8 is an illustration of another interface providing actionableprogram performance information according to an embodiment of thepresent disclosure.

FIG. 9 is an illustration of a method according to an embodiment of thepresent disclosure.

FIG. 10 is an illustration of a computing environment implementedaccording to an embodiment of the present disclosure.

FIG. 11 is an illustration of a computer implemented according to anembodiment of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawing figures which form a part hereof, and which show byway of illustration specific embodiments of the present invention. It isto be understood by those of ordinary skill in this technological fieldthat other embodiments may be utilized, and that structural, electrical,as well as procedural changes may be made without departing from thescope of the present invention. Wherever possible, the same referencenumbers will be used throughout the drawings to refer to the same orsimilar parts.

Embodiments of the present invention relate to a system and method forpresenting actionable program performance information based on audiencecomponents. In this discussion, the term program will be used primarilywith respect to television programs, however, various applications ofthe systems and methods disclosed herein to other media arecontemplated, including, but not limited to radio, digital videorecorder media, internet radio, internet video, internet streamingcontent, recorded media, virtual reality content, and the like.

Referring now to FIG. 3, an embodiment of a system 300 for presentingactionable program performance information is shown. The system 300 mayinclude a content source 301 configured to provide programming contentto a viewer client 305 via a network 303. In some embodiments, thenetwork 303 may include various systems for distribution of contentincluding any desired combination of hardwired and wirelesscommunication links, including over-the-air broadcast, cable, internet,other network connection systems, and the like, which implement networksand hardware known and used in the related art, including broadcasttechnologies, cable or satellite content distribution systems, internetprotocol (IP), LAN, or other networked technologies, and the like,wherein examples of the distributed programming content include live andrecorded television, movies, online content, music, radio or otheraudio-visual content, and the like. In one embodiment, the contentsource 301 may correspond to a cable television headend receivingvarious television content signals and distributing content signalscorresponding to channels of the various television content signals.

The system 301 may further include a metadata source 302 configured tostore and provide metadata to the viewer client 305, where the metadatais related to the programming content provided by the content source301. The metadata stored and provided by the metadata source 302 may becreated and maintained by the same entity that creates and maintains theprogramming content, or in other embodiments they may be differententities. In the embodiment shown, the metadata source 302 is configuredto provide metadata information to the viewer client 305, however inother embodiments, the metadata may be provided to the content source301 or related terminal directly for packaging and transmission alongwith the programming content to the viewer client 305.

The viewer client 305 may be connected to the content source 301 and/orthe metadata source 302 via the network 303. The viewer client 305 mayinclude any terminal or system configured to receive a content signal,and in some embodiments, the viewer client 305 may be configured todecode the content signal and prepare the signal for presentation of thecontent to a user 307 via an output such as a display 306, audiospeaker, or the like. In some embodiments, the viewer client 305 mayalso be configured to transmit information via the network 303 regardingthe content programming consumed by the user 307, such as a particulartelevision channel or television program consumed by the user 307.

For example, implementations of the viewer client 305 may includetelevisions, desktop computers, laptop computers, tablet computers,mobile smartphones, personal media devices, cable set top box receivers,satellite television receivers, and the like. In other embodiments, theviewer client 305 may be implemented in more than one connected device.For example, the viewer client 305 may include a dedicated set top boxconfigured to receive and decode content signals for presentation by thedisplay which is connected to a separate viewing meter, such as a LocalPeople Meter (LPM) implemented by Nielsen Corporation. The viewingmeter, such as the LPM, may be configured to collect viewer informationof the user 307 consuming the content programing, where the viewerinformation includes demographic information such as age, sex,ethnicity, occupation, education level, and the like, and record theviewing history or habits of the user 307 based on the content signalsdecoded and presented by the set top box. The viewing meter, orcollectively the viewer client 305 as shown in FIG. 3, may then transmitthe collected audience data back to a server and database forcollection, storage, and analysis.

In the embodiment of FIG. 3, the system 300 further includes aperformance analyzer 304 connected to the network 303. The viewer client305 may transmit the audience data directly to a performance analyzer304 via the network 303, or the viewer client 305 may transmit theaudience data to a server and database (not depicted) maintained by athird party, such as an audience measuring entity such as the Nielsencorporation, and the third party may then provide the audience data tothe performance analyzer 304 via the network. The performance analyzermay also receive information related to the content programming from thecontent source 301 and related metadata information from the metadatasource 302. However, in other embodiments, the performance analyzer mayreceive the audience data, content programming information, and metadatainformation from the viewer client 305 via the network 303. In certainembodiments, the performance analyzer 304 configured in accordance withthe features and aspects disclosed herein may be configured to operatewithin or support a cloud computing environment. For example, a portionof, or all of, the performance analyzer 304 may reside in a cloudserver.

Embodiments of the performance analyzer 304 may be configured to collectthe audience data, content programming information, and metadatainformation and normalize the audience data based on various factors,including content-related factors as well as non-content relatedfactors, to be discussed further below. The performance analyzer 304 maynormalize the audience data to identify the changes in the dataattributable to various components of the programming performance,including seasonality, lead-in, content related tune-in,non-content-related tune-in, and content and non-content relatedtune-out also referred to as viewer retention.

In some embodiments, the performance analyzer 304 may also assignperformance grades to each item of programming content in relation tonormalized audience data for one or more other comparable or competitiveprogramming content. The performance analyzer 304 may also providealerts for a significant event, such as a substantial increase ordecrease in viewing data, such as a sudden drop in viewer retention. Thealert may include information regarding the event, as well as contextualinformation such as text, audio, video, character information, topicinformation, or the content itself corresponding to the event. Thenormalized audience data, alert information, and any other informationgenerated by the performance analyzer 304 may be stored in a combineddatabase (not depicted), configured to store data for review,presentation, analysis, and further use by the performance analyzer. Thecombined database and the performance analyzer may be implemented usingany number and/or types of suitable processors, volatile andnon-volatile memory systems, and/or data storage apparatuses, and thelike, and may further include hardware required to implement thecombined database, including server terminals, remote terminals, networkhardware, and the like.

In some embodiments, the performance analyzer 304 may also be connectedor otherwise be operably coupled to a display configured to displayinformation, such as an LED or LCD display. In other embodiments, theperformance analyzer 304 may be connected to a server computer orimplement server computer hardware itself, to communicate information toa client terminal (not depicted) via the network 303. It will beunderstood by those of ordinary skill in the art that the variouscomponents and aspects of the system 300 and in particular theperformance analyzer 304 may be implemented in different configurationsand arrangements, including cloud computing implementations, whichcontemplate receiving, transmitting, and otherwise communicating data,content, instructions, and the like, between one or more, or all, of thecomponents of the system 300 via the network 303. Further, theperformance analyzer 304 may be implemented with known and existingaudience measurement systems, thereby improving functionality of thevarious audience measuring methods and systems of the audiencemeasurement industry.

Referring now to FIG. 4, an embodiment of the performance analyzer 400is shown. As previously discussed, the performance analyzer 400 mayreceive audience data from an audience data source 401, which mayinclude set top boxes, computers, Nielsen ratings devices, and the likeas discussed. The performance analyzer 400 may receive programmingcontent from a programming content source 402, which may include a cabletelevision headend, internet streaming content distributor, or a viewerclient such as a set top box, and the like as discussed. The performanceanalyzer 400 may also receive content metadata from a content metadatasource 403, which may include a server and metadata database maintainedby a third party, the programming content source 402, or the viewerclient, and the like as discussed.

The received information may be aggregated and cleaned by a dataaggregator 404 of the performance analyzer 400. The data aggregator 404may detect, correct, and/or remove corrupt, inaccurate, incomplete, orirrelevant data from the received information. For example, the dataaggregator 404 may receive the programming content from the programmingcontent source 402 along with data of the content or the content format,such as encoding information, frame rate information, resolutioninformation, play duration, pre-roll, mid-roll or post-roll information,and the like. The programming content may be received in the form of anactual content file, such as a video file in formats including .ts;.flv; .vob; .avi; .wmv; .asf; .rmvb; .mp4; .mpg; .m4v; 0.3gp; and thelike, or information of the programming content may be received in theform of a pointer or URL indicating a location where the programmingcontent is available for retrieval. The data aggregator 404 may storethe received programming content or related information in a relationaldatabase table, or other data storage model, and assign or store apreexisting identifier, such as primary key, to uniquely identify theprogramming content.

The content metadata received from the content metadata source 403 mayinclude inaccurate, incomplete, or corrupt information which is removedfrom the data, and the metadata may also include metadata informationwhich is not relevant or of interest in the performance analysisprocess. For example, the metadata information may include data of theprogramming content such as year of release, copyright information,production information, network information, and the like, which may notprovide relevant data in the analysis of audience performance. Themetadata may be cleaned of any irrelevant data points and the remainingcleaned metadata may be stored in a relational database table or thelike including an assigned or created primary key identifying thecontent associated with the metadata.

Similarly, the data aggregator 404 may perform data cleaning using theaudience data received from the audience data source 401. The audiencedata may also include inaccurate, incomplete, or corrupt informationwhich is removed from the data by the data aggregator 404, and mayfurther include audience data which is not relevant or of interest inthe performance analysis process. For example, the audience data mayinclude file format information, audience data entity information,audience meter device information, audience location, and the like,which may not provide relevant data in the analysis of audienceperformance. The audience data may be cleaned of any irrelevant datapoints and the remaining cleaned audience data may be stored in arelational database table or the like including an assigned or createdprimary key identifying the content associated with the metadata.

The cleaned data may be provided to the data normalizer 405 which isconnected or operably coupled to a predictive models unit 406, a visualdata models unit 407, and a language data models unit 408. The modelunits 406, 407, 408 may be configured to analyze, store, and providecontextual data to the data normalizer 405 for analysis of the audiencedata, programming content data, and the content metadata. These modelunits 406, 407, 408 may identify, verify, and capture temporal metadatafrom video, as well as derive and identify entities from associatedtime-coded text (for example, closed captioning streams) to verifyon-screen characters or entities (e.g., people, places, objects, and thelike) and determine logical content segments and/or boundaries andrelated topics for identification, tagging, and association withaudience data components.

These segments may be identified using methods and systems known in theart, such as metadata flags, pixel density transitions, filters,phonetic cues, facial and object recognition, audio recognition, and thelike. Further, for text contained within the video, a lexical approachmay incorporate tokenization, alignment with existing taxonomies, topicmodels, and the like. For example, entities such as people, places, andobjects may be identified and extracted based on closed-captioninginformation and audio indexed from the video stream.

Such techniques may also reference existing video, audio, and imagelibraries which provide information for each identified entity that hasappeared in previous broadcasts or episodes of the same programmingcontent, or in other programming content known to the libraries. Forexample, the libraries may include audio, video, and image informationfor every news broadcast personality who has appeared in a particularnews broadcast in the past. However, the libraries may also includeaudio, video, and image information for entities sourced from a widerange of content, including television, movies, streaming video andaudio, music, books, online content, social media content, and the like.In some embodiments, external or third party libraries and databases maybe leveraged for use in these operations, including querying variousdatabases available via a network, such as the internet.

Using combinations of such techniques, the model units may identifysegments of video, for example a video of news broadcast, on variouslevels of granularity, such as story segments, video segments, topicsegments, and word segments. FIG. 5A is a representation of identifiedsegments of a programming content video according to one embodiment. Asshown, the video may comprise one or more story segments 500 whichrepresent one story or topic discussed during the news broadcast. Eachstory segment 500 may comprise video segments 501 which represent clipsof continuous video that are included in each story segment 500.Further, each video segment 501 may comprise multiple topic segments 502which represent discussion of particular topics within each videosegment 501. Further still, each topic segment 502 is segmented intoword or sentence segments 503 which represent the speech or textassociated with the discussion or presentation of each topic segment502.

FIG. 5B is an example of a video of programming content broken up intoidentified story segments 504, 505. The story segments may be identifiedusing the techniques as discussed above, including facial and objectrecognition, audio recognition, text recognition, content metadata, andthe like. Each story segment may correspond to a particular news eventbeing presented during the news broadcast. As shown, the story segment504 may include video segments 506, 507, 508 and the story segment 505may include video segments 509, 510, 511, 512. Although shown as asingle frame in FIG. 5B, it will be understood that the single frames ofvideo segments such as 506 may represent a portion of video comprisingnumerous frames. Information regarding previous broadcasts or episodesof the news broadcast may also be leveraged to identify video segments,topic segments, and the like. For example, visual or audio cues used tobreak up video segments or topic segments for a viewer in previousbroadcasts or episodes may be stored and referenced to identify videoand topic segments encountered in the present programming content video.

Further as shown in FIG. 5B, face detection or face verification 513 maybe leveraged to identify faces that appear within the video segments.Based on the detection times of the faces within the video segments, thetopic segments (not depicted) may be identified and tagged. In otherembodiments, face detection may be leveraged in combination with otherdetection methods, such as audio detection, speech recognition, objectrecognition, text recognition, and the like as previously discussed toidentify, tag, and segment the video segments to topic segments. Furtheras discussed, the topic segments may further be identified and segmentedinto smaller segments, such as sentence segments, word segments, and thelike.

Thus referring back to FIG. 4, model units 406, 407, 408 may beconfigured to identify content based segments of programming content toleverage, store, and provide content based information to the datanormalizer 405. The model units 406, 407, 408 may generate and outputtemporal metadata based on the analysis of the programming content,including information such as asset information (for example a uniqueidentifier), time-code information (relative position in time within thevideo), and metadata type information (identifier for segmentscomprising video, image, audio, text, and the like), related metadatainformation (for example, secondary data in pair values to describe thevideo based on detected image, audio, text, and the like).

Referring back to FIG. 4, the predictive models unit 405 may include orbe operably coupled to a memory, and be configured to store, generate,and provide historical data related to an aspect of the programmingcontent, audience data, or the metadata. For example, the predictivemodels unit 405 may store information related to historical audiencedata for programming content that was previously distributed on the sameday of the month, week, or year as the programming content, or waspreviously distributed at the same time of day as the programmingcontent. This historical audience data may provide information onaudience behavior and habits particular to a particular date or time foranalyzing and providing a predictive model for the audience data.

For example, the predictive models unit 405 may be provided withinformation identifying a time slot of 9:00 a.m. on a Monday inSeptember, or other information which may indicate the broadcast timefor the programming content. Based on the provided broadcast time, thepredictive models unit 405 may provide the data normalizer 405 withaudience data for content which was broadcast at 9:00 am on the previous20 Monday mornings, or audience data for content which was broadcast onthe previous eight Monday mornings of September, and the like. This isprovided as one example, however in other embodiments the predictivemodels unit 405 may be configured to provide the data normalizer 405with audience data for any defined time period or to meet any definedconditions.

For example, the audience data provided by the predictive models unit405 could include data based on prior seasons of the same programmingcontent, particular broadcasts of the programming meeting certaincriteria (season premiere, season finale, special episodes, and thelike), particular broadcasts of the programming from a particular timeperiod, and the like. In other examples, the audience data provided mayinclude data which relate to other programming which meet certaincriteria, such as programming having similar metadata, similar intendedaudience, same or similar broadcast days or times, and the like. Someembodiments of the predictive models unit 405 may be configured toreceive set conditional filter values, in some cases input by a user ofthe system, to identify and return audience data to provide to the datanormalizer 405.

In other embodiments, the predictive models unit 405 may provideprobability models or probability distributions for predicting audiencebehavior based on various factors. For example, the predictive modelsmay provide that during a 30-minute time slot on Monday at 9:00 am,based on historical models of the same time slot, 4% of impressions maybe attributable to seasonality, and 78% of impressions may beattributable to lead-in. Such probability models may provide the datanormalizer 405 with information to compare normalized data and performregression analysis to provide confirmation of output results. Thepredictive models unit 405 may further be configured to receive andstore output results data from the data normalizer 405 for its additionto stored historical data, to update predictive models, and also toprovide back to the data normalizer 405 for future programming contentanalysis.

The visual data models unit 407 may include or be operably coupled tomemory, and be configured to store, generate, and provide informationrelated to visual information of the programming content. For example,the visual data models unit 407 may store information based on facialrecognition, object recognition, text recognition, movement recognition,prominently displayed colors, and the like, and may also store audiencedata relative to the stored visual data models information.

For example, the visual data models unit 407 may store historicalaudience data associated with a particular television personality,including audience tune-out data when the personality is featured onprogramming content. In another example, the visual data models unit 407may store historical audience data associated with a particular objectsuch as a landmark or monument, including audience tune-out when thelandmark or monument appears on screen, and the like.

The visual data models unit 407 may also be configured to perform visualmodeling of the programming content information received from the dataaggregator 404 to identify segments within the video based on visualinformation as previously discussed, as well as leveraging audio, text,and metadata information in combination with visual information. Furtherthe visual data models unit 407 is configured to recognize faces,objects, text, colors, and any other visual information of theprogramming content according to methods and systems for visual datarecognition known to those of ordinary skill in the related art. Thecontent metadata received from the content metadata source 403 may alsobe used to identify and recognize visual data related to the programmingcontent.

For example, provided with the programming content information and/orthe content metadata, the visual data models unit 407 may identify aparticular actor that is featured in the content. The visual data modelsunit 407 may then provide the data normalizer 405 with informationrelated to the actor appearing in the programming content, such asinformation of the actor, time segments of the programming contentduring which the particular actor appears, length of time the actorappears, and the like. Further, embodiments of the visual data modelsunit 407 is further configured to perform facial identification, facialverification using various reference sources, scoring of facialrecognition instances, additional training of facial identificationusing said various reference sources, and the like.

In other embodiments, the visual data models unit 407 may be configuredto retrieve historical audience data associated with the particularactor, either from memory included in the visual data models unit 407 oroperably coupled thereto. In other embodiments, the historical audiencedata associated with visual data models may be stored by and retrievedfrom the predictive models unit 406, or other storage memory. Thus, thevisual data models unit 407 may provide information on all previousappearances of the particular actor along with audience data,specifically audience tune-out, at each particular time point duringwhich the actor appears. Thus the historical data may provide context toaudience performance data of the current programming content.

Provided with visual data modeling information related to a particularactor and historical audience data associated with the particular actor,the data normalizer 405 may be configured to identify significantchanges in the audience data received from the audience data source 401that is related to the actual content of the programming content, e.g.,the appearance of the actor. This identification is able to distinguish,and remove if desired, activity which is driven by random factors notrelated to the actual content of the programming content being consumedby the user.

As another example, if a particular segment of programming contentfeatures a popular singer performing a hit song, the visual data modelsunit 407 may identify the singer's face and/or body and provide the datanormalizer 405 with information on the singer's appearance within theprogramming content. The data normalizer 405 may also be provided withhistorical audience data associated with the singer, or the datanormalizer 405 may retrieve the historical audience data from thepredictive models unit 406 or the visual data models unit 407.

The data normalizer 405 may then associate an increase in tune-outnumbers within the audience data to the content of the programmingcontent, specifically the singer's appearance, and generate acontent-based indicator to be associated with the increase in tune-outnumbers at that particular time point, or alternatively may categorizethe increase in tune-out numbers as content-based, specificallyassociated with the singer's appearance. Similarly, the data normalizermay perform similar operations for an increase in audience retentionduring the segment of the singer's appearance in the programmingcontent, and generate a content-based indicator to be associated withthe increase in audience retention, or alternatively may categorize theincrease in audience retention during as content-based, specificallyassociated with the singer's appearance.

Similarly, the language data models unit 408 may include or be operablycoupled to a memory, and be configured to store, generate, and provideinformation related to language and audio information of the programmingcontent. The language data models unit 408 may store information basedon audio detection, speech recognition, text recognition, voicerecognition, sound analysis, and the like, and may also store audiencedata relative to the stored language data models information.

For example, the language data models unit 408 may store historicalaudience data associated with a particular discussion topic, includingaudience tune-out data when the discussion topic is featured onprogramming content. In another example, the language data models unit408 may store historical audience data associated with a particular songwithin the content, including audience retention loss when the song isplayed, audience retention gain when the song is played, and the like.

The language data models unit 408 may also be configured to performlanguage modeling of the programming content information received fromthe data aggregator 404 to identify and recognize visual text within thecontent, audible speech, songs, sounds, closed captioning information ofthe programming content, and the like. The content metadata receivedfrom the content metadata source 403 may also be used to identify andrecognize language data related to the programming content. For example,this may incorporate components for facilitating tokenizing text ofaudio as well as phonetic analysis providing a breakdown of unique“speakers” as derived from the audio stream and aligned by time-code andmetadata.

This may include speaker diarization which corresponds to the process ofpartitioning an input audio stream into homogenous segments accordingthe speaker identity and detecting syllable nuclei from the audio streamin order to measure speech rate. These and other recognition operationsmay be accomplished using methods and systems known to those of ordinaryskill in the art, for example speaker recognition and identificationsystem tools in conjunction with phonetics analysis libraries.

Such embodiment would provide temporal metadata for each given episodeby identifying a unique speaker, time-code within the episode, syllablecount for the corresponding time segment, and average amplitude of thespoken voice for the time segment in decibels. The language data modelsunit 408 may further identify a main speaker and secondary speakersbased on who has the highest percentage of episode time assigned, suchas a news anchor speaking for the majority of a news broadcast.

For example, provided with the programming content information and/orcontent metadata information, the language data models unit 408 mayidentify a particular political topic that is being discussed during atelevision talk show. The language data models unit 408 may then providethe data normalizer 405 with information related to the political topicbeing discussed in the programming content, such as information of thetopic, time segments of the programming content during which the topicis discussed, length of time the topic is discussed, identity of themain speaker discussing the topic, volume and tone of voice, inflectionof words being used to discuss the topic, average amplitude of thevoice, and the like.

In other embodiments, the language data models unit 408 may beconfigured to retrieve historical audience data associated with thetopic, either from memory included in the language data models unit 408or operably coupled thereto. In other embodiments, the historicalaudience data associated with language data models may be stored by andretrieved from the predictive models unit 406, or other storage memory.

Similar to the visual data models information discussed above, providedwith language data modeling information related to a particular topicbeing discussed in the programming content and historical audience dataassociated with the particular topic, the data normalizer 405 may beconfigured to identify significant changes in the audience data receivedfrom the audience data source 401 that is related to the actual contentof the programming content, e.g., the discussion of the topic.Conversely, this identification is able to distinguish activity which isdriven by more random factors not related to the actual content of theprogramming content being consumed by the user.

As another example, if a particular segment of programming contentfeatures audio of a popular song, the language data models unit 408 mayidentify the words, tone, melody, beat pattern, audio fingerprint, andthe like of the popular song using methods and systems for audiorecognition known to those in the art, and provide the data normalizer405 with information on the song's audio within the programming content.The data normalizer 405 may also be provided with historical audiencedata associated with the song, or the data normalizer 405 may retrievethe historical audience data from the predictive models unit 406 or thelanguage data models unit 408.

The data normalizer 405 may then associate a audience tune-out to thecontent of the programming content, specifically the audio of the song(or associated performance of the song by a singer), and generate acontent-based indicator to be associated with the decrease in retentionnumbers at that particular time point, or alternatively may categorizethe audience tune-out as content-based, specifically associated with thesong's audio. Similarly, the data normalizer may perform similaroperations for an increase in audience tune-in during the segment of thesong's audio in the programming content, and generate a content-basedindicator to be associated with the increase in audience tune-in, oralternatively may categorize the increase in audience tune-in ascontent-based, specifically associated with the song audio.

Based on the categorization of audience data events, the data normalizer405 may distinguish between content-driven tune-in or tune-out,non-content-driven tune-in or tune-out, shift in audience data due toseasonality, and the like. This provides for isolated audience datainformation and the various components that make up the audience datamay be identified for a particular programming content and compared tothe audience data of comparable programming content.

For each given program, the data normalizer 405 may calculate thecontent and non-content driven components of the audience data, forexample, the sum of minute-to-minute percent losses and gains duringcontent of the program and non-content of the program, or associatedwith a content-based audience event determined using the inference andpredictive models unit, visual data models unit, and the language datamodels unit. The data normalizer 405 may also calculate rollingstatistics throughout the duration of the program, such as the mean andstandard deviation for each of the components of the audience data todetermine the norm or predicted behavior of the audience for theparticular program. The mean and standard deviation for each of theaudience data components may be determined over a given number ofobservations, for example over the last forty broadcasts or episodes ofthe same program.

In some embodiments, the data normalizer 405 is also configured tonormalize and assign standard grades for each component of the audiencedata using the calculated mean and standard deviation values withrespect to comparable programming content, for example content which wasbroadcast during the same time slot on a different channel. The datanormalizer 405 may employ a standardized score analysis over adistribution including the audience data of the comparable programmingcontent.

For example, the data normalizer 405 may determine that the component ofthe audience data corresponding to content-driven retention loss (sum ofminute over minute percent losses by audience tune-out during content)for a particular program was 11%. Standardized over a distributionincluding the content-driven retention loss of comparable programmingcontent, the data normalizer may assign a z-score of 0.45.

The following is provided as an example for the standardized scoreanalysis. For televised programming content, lead-in audience data mayonly be available at the first minute (or Minute of Program 1, “MOP”1=0) for shows preceded by a nationally broadcast program. Therefore,when programming content is preceded by local programming, audience datacarried over from the local programming may be included in the tune-incategory. To remove variation between programs preceded by localprogramming or national programming, the lead-in component may bereplaced by a “Start” component, which equals the sum of tune-in andlead-in audience data. Based on this, an example of a mathematicalrepresentation of the z-score calculation for the start component may beas follows, where r=reach, t=MOP, s=start, u=tune-in, w=switch-in, andd=lead-in:

t = 1 r₁ = ∑ u₁w₁d₁${{{where}\mspace{14mu} u} = {{tune}\text{-}{in}}},{w = {{switch}\text{-}{in}}},{{{and}\mspace{14mu} d} = {{{lead}\text{-}\left. {in}\downarrow S \right.} = {{u_{t} + \left. d_{t}\downarrow Z_{S} \right.} = \frac{S - \mu_{S}}{\sigma_{S}}}}}$

Similarly, examples of mathematical representations of the z-scorecalculations for changes in retention for content-driven gain (Gc),non-content driven gain (Gn), content-drive loss (Lc), and non-contentdriven loss (Ln) during the programming content for MOP 2 (after start)through the end of the program's duration (p), may be as follows:

2 ≦ t ≦ p − 1: r_(t) = f(g_(t, y), l_(t, y))${{where}\mspace{14mu} y} = {c\mspace{14mu}({content})\mspace{14mu}{or}\mspace{14mu} n\mspace{14mu}\left. ({noncontent})\downarrow\begin{matrix}{G_{c} = {\sum\limits_{t = 2}^{p - 1}\; g_{t,c}}} & {G_{n} = {\sum\limits_{t = 2}^{p - 1}\; g_{t,n}}} & {L_{c} = {\sum\limits_{t = 2}^{p - 1}\; l_{t,c}}} & {L_{n} = {\sum\limits_{t = 2}^{p - 1}\; l_{t,n}}}\end{matrix}\downarrow\begin{matrix}{Z_{G_{c}} = \frac{G_{c} - \mu_{G_{c}}}{\sigma_{G_{c}}}} & {Z_{G_{n}} = \frac{G_{n} - \mu_{G_{n}}}{\sigma_{G_{n}}}} & {Z_{L_{c}} = \frac{L_{c} - \mu_{L_{c}}}{\sigma_{L_{c}}}} & {Z_{L_{n}} =}\end{matrix} \right.\frac{L_{n} - \mu_{L_{n}}}{\sigma_{L_{n}}}}$

In some embodiments, the data normalizer 405 may assign a letter grade,referred to herein as a component grade, corresponding to the z-scoreaccording to a distribution according to Table 1, below, shown by way ofexample only:

TABLE 1 Z-Score Grade Z > 0.67 A 0 < Z ≦ 0.67 B −0.67 < Z ≦ 0     C   Z≦ −0.67 D

The z-scores or corresponding component grades may be output by the datanormalizer 405, as discussed further below, and may also be stored in anoutput database to be associated with the programming content, orvarious other related data points, including the content metadata,broadcast time, and the like. In some embodiments, the z-scores and/orthe component grades may be provided to the predictive models unit 406for storage and later retrieval.

Referring now to FIG. 6, the performance analyzer 600 may outputnormalized audience data, component audience data, z-scores forcomponent audience data, component grades for audience data, and thelike. For example, the performance analyzer 600 may output the componentgrades to the performance grading unit 601. The performance grading unitmay include or be otherwise coupled to a memory configured to store thecomponent grade information. The component grade information may bestored in a relational database or other data storage model, and may beassociated with information related to the programming content, such asthe programming content itself, content metadata, broadcast time, andthe like. The performance grading unit may further be configured tooutput the stored information to an output of performance data 605,which may include an interface, a display, another database for furtheraggregation and compiling, or a terminal for processing.

The performance analyzer 600 may further output performance alerts basedon the normalized audience data or component audience data information.The performance alerts may be generated based on preset rules foralerts, such as a threshold range of variance in content-driven ornon-content driven shift in audience data. For example, an alert may bepreset to be generated whenever content-driven retention loss exceeds 5%within one minute. Such a drastic change in content-based retention maywarrant evaluation of the content of the programming content beingpresented at that time to cause such retention loss. The performanceanalyzer 600 may thus generate an alert record, including informationlogging the details of the programming content, audience datainformation, broadcast information, and any other information related tothe alert event.

The alert event information may be output to the performance alerts unit602, which may include a memory or otherwise be operably coupled to amemory configured to store alert information. The alert eventinformation may be stored in a relational database or other data storagemodel, and may be associated with information related to the alert, suchas the programming content at the time of the event, audience data,content metadata, and the like. The performance alert unit 602 mayfurther be configured to output the stored information to an output ofperformance data 605, which may include an interface, a display, audiooutput, a transmitting unit for transmission, a database for furtheraggregation and compiling, output to a terminal, or the like.

The performance analyzer 600 may also output content-driven audiencedata and non-content driven audience data, as well as contextualinformation to content optimization unit 603 and non-contentoptimization unit 604, respectively. The content optimization unit 603may include a memory or otherwise be operably coupled to a memoryconfigured to store information related to the content-driven audiencedata, programming content, content metadata, visual data modeling andlanguage data modeling information, and the like. The contentoptimization unit 603 may be configured to store and compile theinformation, process the information to detect trends, determineprobabilities for content-driven audience data, determine predictivemodels for improving content-driven audience data, and the like. In someembodiments, the content optimization unit 603 may further be configuredto output the stored information to an output of performance data 605,which may include an interface, a display, another database for furtheraggregation and compiling, or a terminal for processing.

For example, the content optimization unit 603 may provide informationon how a particular video content discussing a certain news topic can beoptimized. Based on an input of the news topic, the content optimizationunit 603 may provide audience data component information and/or gradesfor each news personality who has presented programming contentdiscussing the particular news topic. Accordingly, a content creator maybe able to determine which news personality would be received mostpositively by the audience, and thus optimize the content for audienceretention.

As discussed, the audience component information or assigned grades canbe based on correlated audience component data with content analysis viafacial recognition, voice recognition, speech analysis, textrecognition, and the like, as discussed. In another example, the contentoptimization unit 603 may provide various broadcast times during whichthe audience retention is the highest for the particular news topic,which may provide actionable performance information to the contentcreator or distributor to further optimize audience performance of thecontent. Thus, the performance analyzer 600 may improve functionality ofvarious audience measuring methods and systems of the audiencemeasurement industry, whereby content creators or distributors areprovided with actionable program performance information and directionto create or distribute content.

Similarly, the non-content optimization unit 604 may include a memory orotherwise be operably coupled to a memory configured to storeinformation related to the non-content-driven audience data, programmingcontent, content metadata, visual data modeling and language datamodeling information, and the like. This information may be to analyzedto determine alterations in non-content to improve audience retention,for example, altering the programming content just prior to commercialbreaks or non-content segments to prevent loss in retention of audiencedata. Accordingly, the non-content optimization unit 604 may provideaudience data component information related to non-content factors whichaffect audience data such as visual graphics, colors, transitions,texts, fonts, various metadata of advertisements such as intendeddemographic, types of products, style of marketing, MOP ofadvertisement, and the like.

The non-content optimization unit 603 may be configured to store andcompile the information, process the information to detect trends,determine probabilities for non-content-driven audience data, determinepredictive models for improving non-content-driven audience data, andthe like. In some embodiments, the non-content optimization unit 603 mayfurther be configured to output the stored information to an output ofperformance data 605, which may include an interface, a display, anotherdatabase for further aggregation and compiling, or a terminal forprocessing.

Referring now to FIG. 7, an embodiment of output performance data to aninterface 700 is shown. In the depicted embodiment, information relatedto audience data may be displayed in conjunction with the programmingcontent and various representations of audience data components. Theinterface 700 may include a graphical display of various audience datacomponent information, including content and non-content tune-out data703 which may provide details on the other programming content thataudience viewers switch to when tuning-out from the subject programmingcontent 701. The interface 700 may also include information on tune-in(and switch-in) 702, including details on the other programming contentthat audience viewers are switching from when tuning-in to the subjectprogramming content 702. The tune-out data 703 and tune-in data 702 maybe displayed in a graphical user interface, for example bar graphs asshown in FIG. 7, however other embodiments of displaying the tune-in andtune-out information are contemplated by this disclosure.

The tune-in data 702 and tune-out data 703 may be continually updated tocorrespond to the current playback position of the programming content701. Additionally, the interface 700 may include other audience datainformation or calculated information in additional user interfaceregion 705 which includes MOP, historical audience loss percentage basedon inference and predictive models previously discussed, currentaudience loss percentage, assigned content grade, assigned non-contentgrade, and the like. The information presented in additional userinterface 705 also may be continually updated to correspond to thecurrent playback position of the programming content 701.

The interface 700 may further include a graphical audience progression704 presenting a progression of audience data component information,such as tune-in (audience gain) 706 and tune-out (audience loss) 707.The audience progression 704 may include information on content ornon-content based audience data over time for the programming content,allowing for identification of notable increases or decreases 710 inconjunction with the content related to each data change. The embodimentshown in FIG. 7 shows tune-in and tune-out audience data in thegraphical audience progression 704, however in another embodiment, thegraphical audience progression 704 may show only one, or in some casesmore than two audience data components. The audience progression 704interface may further be configured to present audience data componentinformation of one or more comparable programming content presented onthe same interface for comparison and analysis.

Embodiments of the graphical audience progression 704 may also includevisual information on how the current audience progression informationcompares to historical audience data based on the data generated by thedata normalizer as previously discussed. For example, the graphicalaudience progression 704 may include historical range indicatorsrepresenting one or more confidence intervals based on the historicaldata, corresponding to estimates of the audience data for the currentaudience data of the programming content 701. The graphical audienceprogression 704 depicts the display of interval ranges corresponding to50% (708) and 90% (709) confidence intervals for estimation of thecurrent audience data. The interval ranges may also be used to identifysignificant outliers 710 from the expected historical data.

Thus, outliers falling outside of these historical data based intervalsmay provide valuable information for identifying important audience dataevents, such as a large content-based tune-out event. Conversely, thisalso provides valuable information for evaluating audience data eventswithin the context of historical data, for example, being able todetermine that a large tune-out event was to be expected at a particularMOP given historical audience data and thus not cause for concern oralarm for the content creator.

The interface 700 may also be configured to provide instant alignment ofdata in response to a user input selecting a particular MOP of theprogramming content 701. For example, a user may select a particular MOPbased on the graphical audience progression 704 to view details relatedto a large drop in audience retention. In response to a user inputselecting the particular MOP, either by a selection to the graphicalaudience progression, drag of a playback progress indicator, manualentry of a particular MOP, or the like as known in the related art, theinterface 700 may present the programming content 701, tune-in data 702,tune-out data 703, additional user interface region 705, and the likewith updated content and data corresponding to the selected MOP. Thus auser may be able to jump directly to a particular MOP of interest basedon a specific point shown by the graphical audience progression 704 orother information, to obtain all related audience data information.

The information presented in interface 700 may thus provide actionabledata regarding the audience performance of the programming content. Thesystem may provide direct correlation information, along with visualdata and interactive functionality for a content creator to evaluateaudience reaction to particular content, such as people, topics, songs,tone of voice, locations, objects, and the like. Based on the actionableperformance data, content creators and distributors may optimize contentcreation or distribution to improve audience data for particularprogramming content, for example by increasing featured time forparticular personalities associated with a strong hold on audienceretention, discussing popular topics during longer segments, featuringmore popular songs or audio during particular segments, and the like.Content distributors may utilize the actionable data by alteringbroadcast times, rearranging lineups for particular programming orsegments of content, altering the timing of advertising breaks orcontent, or altering programming content offerings on various channelsbased on an analysis of the audience data components, such asseasonality, lead-in (or start), content-based retention, or the like.

FIG. 8 shows another embodiment of output performance data to aninterface 800. In the depicted embodiment, two comparable programmingcontent items 801, 802 may be simultaneously displayed for performancecomparison. The interface 800 may be configured to present correspondingaudience data component information for each of the programming contentitems 801, 802. For example, corresponding content-based retentionaudience data may be displayed for each of the programming content items801, 802 which may have both been broadcast during the same time slot onthe same day.

In another example as depicted in FIG. 8, the interface 800 may presentone or more audience data component information for one of theprogramming content items 801, 802. For example, the interface 800 maypresent an interface 803 including net impressions progressioninformation 804, 805 for the programming content 801 during thebroadcast time period. In the example of FIG. 8, the interface 803 mayalso include graph lines 806, 807 representing the average netimpressions corresponding to each programming content 801, 802 toprovide additional context for the net impressions information 804, 805.

Additionally, another interface 808 for content or non-content basedretention progression information may also be presented corresponding tothe programming content 801 or 802, such as tune-in (audience gain) 809and tune-out (audience loss) 810. The retention progression informationinterface 808 may include visual information 811, 812 on how the gainand loss progression information compares to historical audience databased on the data generated by the data normalizer as previouslydiscussed. For example, the retention progression information interface808 may include historical range indicators 811, 812 representing one ormore confidence intervals based on the historical data, corresponding toestimates of the current audience data of the programming content 801 or802. For example, the retention progression information interface 808depicts the display of interval ranges corresponding to 50% (811) and90% (812) confidence intervals for estimation of the audience gain andloss data, similar to the interface 700 discussed in FIG. 7.

Each interface 803, 808 may receive various inputs and change thedisplayed information accordingly. For example, a user input to aparticular MOP of interfaces 803 or 808 may cause all information ininterface 800 to display corresponding programming content at 801, 802and corresponding audience data information in interfaces 803, 808according to the selected MOP. Alternatively, a user input to playbackcontrols of programming content 801, 802 may similarly result ininterfaces 803, 808 being changed to correspond to changes in theplayback, including a particular selected MOP, playback speed, playbackorder, and the like. The embodiments of inputs to the interface 800 arenot limited to this discussion and it will be understood that variousinputs and display features of the interface 800 are considered. Forexample, other inputs contemplated include zoom-in/zoom-out inputs tovarious portions of the interfaces 803, 808, bookmarking a particularMOP of interest in programming content 801, 802 or to the interfaces803, 808, changing the historical intervals 811, 812, presented,displaying or hiding comparable programming content and related audiencedata, changing the scale or axes of interfaces 803, 808, and the like.

Such an interface embodiment may provide actionable detailed informationby presenting content or non-content based audience data in correlationwith the actual content of the programming content 801, whileadditionally presenting a comparable programming content item 802 toevaluate audience movement between programming, shifts in audienceviewing due to content, and audience reactions to specific people,objects, topics, audio, and the like.

A method 900 according to an embodiment of the present disclosure isshown in FIG. 9. The embodiment may include obtaining primary contentinformation related to first content at operation 901, wherein the firstcontent is distributed to a plurality of viewers during a particulartime duration and the primary content information comprises audio andvideo of the first content. The method further includes obtainingsecondary content information related to the first content 902, whereinthe secondary content information comprises information identified basedon the first content, and obtaining activity information of theplurality of viewers of the first content during the particular timeduration 903.

Further, the embodiment of the method includes normalizing the activityinformation of the plurality of viewers for each of a plurality ofaudience components based on the primary content information and thesecondary content information 904, storing the normalized activityinformation for each of the plurality of audience components to beassociated with the first content, 905, and displaying data of thenormalized activity information of at least one of the plurality ofaudience components at a first time point within the particular timeduration along with a portion of the first content at the first timepoint 906.

As previously discussed, in some embodiments the performance analyzermay comprise one or more software or hardware computer systems and mayfurther comprise or be operably coupled to one or more hardware memorysystems for storing information including databases for storing,accessing, and querying various content, audience data, metadata, andthe like. In hardware implementations, the one or more computer systemsincorporate one or more computer processors and controllers.

The components of the various embodiments discussed herein may eachcomprise a hardware processor of the one or more computer systems, andin one embodiment a single processor may be configured to implement thevarious components. For example, in one embodiment, the predictive datamodels unit, the visual data models unit, and the language data modelsunit may be implemented as separate hardware systems, or may beimplemented as a single hardware system. The hardware system may includevarious transitory and non-transitory memory for storing information,wired and wireless communication receivers and transmitters, displays,and input and output interfaces and devices. The various computersystems, memory, and components of the system 300 may be operablycoupled to communicate information, and the system may further includevarious hardware and software communication modules, interfaces, andcircuitry to enable wired or wireless communication of information.

In selected embodiments, the features and aspects disclosed herein maybe implemented within a computing environment 1000 shown in FIG. 10,which may include one or more computer servers 1001. A server 1001 maybe operatively coupled to one or more data stores 1002 (e.g., databases,indexes, files, or other data structures). A server 1001 may connect toa data communication network 1003 comprising a local area network (LAN),a wide area network (WAN) (e.g., the Internet), a telephone network, asatellite or wireless communication network, or some combination ofthese or similar networks.

One or more client devices 1004, 1005, 1006, 1007, 1008 may be incommunication with the server 1001, and a corresponding data store 1002via the data communication network 1003. Such client devices 1004, 1005,1006, 1007, 1008 may include, for example, one or more laptop computers1007, desktop computers 1004, smartphones and mobile phones 1005, tabletcomputers 1006, televisions 1008, or combinations thereof. In operation,such client devices 1004, 1005, 1006, 1007, 1008 may send and receivedata or instructions from or to the server 1001, in response to userinput received from user input devices or other input. In response, theserver 1001 may serve data from the data store 1002, alter data withinthe data store 1002, add data to the data store 1002, or the like orcombinations thereof.

In selected embodiments, the server 1001 may stream or transmit one ormore video files including video content, audio content, and/or metadatafrom the data store 1002 to one or more of the client devices 1004,1005, 1006, 1007, 1008 via the data communication network 1003. Thedevices may output video content from the video file using a displayscreen, projector, or other video output device. For example, the videofile may comprise a clip of a movie, television show, live programming,news broadcast, or portion thereof for output using a display device ofone or more of the client devices. In certain embodiments, the system300 configured in accordance with the features and aspects disclosedherein may be configured to operate within or support a cloud computingenvironment. For example, a portion of, or all of, the data store 1002and server 1001 may reside in a cloud server.

Referring to FIG. 11, an illustration of an example computer 1100 isprovided. One or more of the devices 1004, 1005, 1006, 1007, 1008 of thesystem 1000 may be configured as or include such a computer 1100. Inselected embodiments, the computer 1100 may include a bus 1103 (ormultiple buses) or other communication mechanism, a processor 1101, mainmemory 1104, read only memory (ROM) 1105, one or more additional storagedevices 1106, a communication interface 1102, or the like orsub-combinations thereof. The embodiments described herein may beimplemented within one or more application specific integrated circuits(ASICs), digital signal processors (DSPs), digital signal processingdevices (DSPDs), programmable logic devices (PLDs), field programmablegate arrays (FPGAs), processors, controllers, micro-controllers,microprocessors, other electronic units designed to perform thefunctions described herein, or a selective combination thereof. In allembodiments, the various components described herein may be implementedas a single component, or alternatively may be implemented in variousseparate components.

A bus 1103 or other communication mechanism, including multiple suchbuses or mechanisms, may support communication of information within thecomputer 1100. A processor 1101 may be connected to a bus 1103 andprocess information. In selected embodiments, a processor 1101 may be aspecialized or dedicated microprocessor configured to perform particulartasks in accordance with the features and aspects disclosed herein byexecuting machine-readable software code defining the particular tasks.Main memory 1104 (e.g., random access memory—or RAM—or other dynamicstorage device) may be connected to a bus 1103 and store information andinstructions to be executed by a processor 1101. Main memory 1104 mayalso store temporary variables or other intermediate information duringexecution of such instructions.

Read only memory 1105 (ROM) or some other static storage device may beconnected to a bus 1103 and store static information and instructionsfor a processor 1101. An additional storage device 1106 (e.g., amagnetic disk, optical disk, memory card, or the like) may be connectedto a bus 1103. The main memory 1104, ROM 1105, and the additionalstorage device 1106 may include a non-transitory computer-readablemedium holding information, instructions, or some combination thereof,for example instructions that when executed by the processor 1101, causethe computer 1100 to perform one or more operations of a method asdescribed herein. A communication interface 1102 may also be connectedto a bus 1103. A communication interface 1102 may provide or supporttwo-way data communication between a computer 1100 and one or moreexternal devices (e.g., other devices contained within the computingenvironment).

In selected embodiments, a computer 1100 may be connected (e.g., via abus) to a display 1107. A display 1107 may use any suitable mechanism tocommunicate information to a user of a computer 1100. For example, adisplay 1107 may include or utilize a liquid crystal display (LCD),light emitting diode (LED) display, projector, or other display deviceto present information to a user of the computer 1100 in a visualdisplay. One or more input devices 1108 (e.g., an alphanumeric keyboard,mouse, microphone) may be connected to a bus 1103 to communicateinformation and commands to a computer 1100. In selected embodiments,one input device 1108 may provide or support control over thepositioning of a cursor to allow for selection and execution of variousobjects, files, programs, and the like provided by the computer 1100 anddisplayed by a display 1107.

The computer 1100 may be used to transmit, receive, decode, display, orthe like one or more video files. In selected embodiments, suchtransmitting, receiving, decoding, and displaying may be in response toa processor 1101 executing one or more sequences of one or moreinstructions contained in main memory 1104. Such instructions may beread into main memory 1104 from another non-transitory computer-readablemedium (e.g., a storage device).

Execution of sequences of instructions contained in main memory 1104 maycause a processor 1101 to perform one or more of the procedures or stepsdescribed herein. In selected embodiments, one or more processors in amulti-processing arrangement may also be employed to execute sequencesof instructions contained in main memory 1104. Alternatively, or inaddition thereto, firmware may be used in place of, or in connectionwith, software instructions to implement procedures or steps inaccordance with the features and aspects disclosed herein. Thus,embodiments in accordance with the features and aspects disclosed hereinmay not be limited to any specific combination of hardware circuitry andsoftware.

Non-transitory computer readable medium may refer to any medium thatparticipates in holding instructions for execution by a processor 1101,or that stores data for processing by a computer, and comprise allcomputer-readable media, with the sole exception being a transitory,propagating signal. Such a non-transitory computer readable medium mayinclude, but is not limited to, non-volatile media, volatile media, andtemporary storage media (e.g., cache memory). Non-volatile media mayinclude optical or magnetic disks, such as an additional storage device.Volatile media may include dynamic memory, such as main memory. Commonforms of non-transitory computer-readable media may include, forexample, a hard disk, a floppy disk, magnetic tape, or any othermagnetic medium, a CD-ROM, DVD, Blu-ray or other optical medium, RAM,PROM, EPROM, FLASH-EPROM, any other memory card, chip, or cartridge, orany other memory medium from which a computer can read.

In selected embodiments, a communication interface 1102 may provide orsupport external, two-way data communication to or via a network link.For example, a communication interface 1102 may be a wireless networkinterface controller or a cellular radio providing a data communicationnetwork connection. Alternatively, a communication interface 1102 maycomprise a local area network (LAN) card providing a data communicationconnection to a compatible LAN. In any such embodiment, a communicationinterface 1102 may send and receive electrical, electromagnetic, oroptical signals conveying information.

A network link may provide data communication through one or morenetworks to other data devices (e.g., client devices as shown in thecomputing environment 1000). For example, a network link may provide aconnection through a local network of a host computer or to dataequipment operated by an Internet Service Provider (ISP). An ISP may, inturn, provide data communication services through the Internet.Accordingly, a computer 1100 may send and receive commands, data, orcombinations thereof, including program code, through one or morenetworks, a network link, and communication interface 1102. Thus, acomputer 1100 may interface or otherwise communicate with a remoteserver (e.g., server 1001), or some combination thereof.

The various devices, modules, terminals, and the like discussed hereinmay be implemented on a computer by execution of software comprisingmachine instructions read from computer-readable medium, as discussedabove. In certain embodiments, several hardware aspects may beimplemented using a single computer, in other embodiments multiplecomputers, input/output systems and hardware may be used to implementthe system.

For a software implementation, certain embodiments described herein maybe implemented with separate software modules, such as procedures andfunctions, each of which perform one or more of the functions andoperations described herein. The software codes can be implemented witha software application written in any suitable programming language andmay be stored in memory and executed by a controller or processor.

The foregoing disclosed embodiments and features are merely exemplaryand are not to be construed as limiting the present invention. Thepresent teachings can be readily applied to other types of apparatusesand processes. The description of such embodiments is intended to beillustrative, and not to limit the scope of the claims. Manyalternatives, modifications, and variations will be apparent to thoseskilled in the art.

What is claimed is:
 1. A method comprising: obtaining primary metadatainformation related to a first video content, wherein the first videocontent is viewed by a plurality of viewers during a particular timeduration; obtaining secondary metadata content information related tothe first video content, wherein the secondary metadata contentinformation identifies specific content presented to the plurality ofviewers during the first video content; obtaining historical audienceinformation based on the primary metadata information and the secondarymetadata content information, wherein the historical audienceinformation indicates previous audience activity associated withparticular content identified by the primary metadata information or thesecondary metadata content information; obtaining audience activityinformation of the plurality of viewers of the first video contentduring the particular time duration, wherein the audience activityinformation includes data for audience tune-in and tune-out of the firstvideo content during the particular time duration; generating data for aplurality of activity components based on the audience activityinformation and the historical audience information; storing thegenerated data for the plurality of activity components to be associatedwith the first video content in a memory; and displaying the generateddata on a display of at least one of the plurality of activitycomponents at a first time point within the particular time durationalong with the first video content at the first time point.
 2. Themethod of claim 1, wherein the plurality of activity factors comprisesat least seasonality, lead-in, content-based tune-ins, non-content-basedtune-ins, content-based tune-outs, or non-content-based tune-outs. 3.The method of claim 1, wherein the secondary metadata contentinformation comprises at least object recognition information identifiedfrom the first video content, facial recognition information identifiedfrom the first video content, speech recognition identified from audioof the first video content, or text information identified from thefirst video content.
 4. The method of claim 1, further comprisingnormalizing at least one of the plurality of activity componentsrelative to corresponding activity components of a plurality of othercontent distributed during the particular time duration.
 5. The methodof claim 3, wherein the secondary metadata content information furthercomprises predictive information related to at least the first videocontent or the particular time duration.
 6. The method of claim 4,further comprising assigning a performance grade to the first videocontent for each of the plurality of activity factors, wherein eachperformance grade corresponds to a standard score relative to theplurality of other content.
 7. The method of claim 4, further comprisingdisplaying an alert related to the at least one activity component whenthe activity component includes activity outside of a predeterminedthreshold activity range.
 8. The method of claim 6, further comprisingdisplaying data of normalized activity components for a second videocontent of the plurality of other content at the first time point alongwith the second content at the first time point.
 9. A system comprising:a memory configured to store information; a display configured todisplay information; a receiver configured to receive information; andone or more controllers configured to: obtain primary metadatainformation related to a first video content via the receiver, whereinthe first video content is viewed by a plurality of viewers during aparticular time duration; obtain secondary metadata content informationrelated to the first video content via the receiver, wherein thesecondary metadata content information identifies specific contentpresented to the plurality of viewers during the first video content;obtain historical audience information based on the primary metadatainformation and the secondary metadata content information, wherein thehistorical audience information indicates previous audience activityassociated with particular content identified by the primary metadatainformation or the secondary metadata content information; obtainaudience activity information of the plurality of viewers of the firstvideo content during the particular time duration via the receiver,wherein the audience activity information includes data for audiencetune-in and tune-out of the first video content during the particulartime duration; generate data for a plurality of activity componentsbased on the audience activity information and the historical audienceinformation; cause the memory to store the generated data for theplurality of activity components to be associated with the first videocontent; and cause the display to display the generated data of at leastone of the plurality of activity components at a first time point withinthe particular time duration along with the first video content at thefirst time point.
 10. The system of claim 9, wherein the plurality ofactivity factors comprises at least seasonality, lead-in, content-basedtune-ins, non-content-based tune-ins, content-based tune-outs, ornon-content-based tune-outs.
 11. The system of claim 9, wherein thesecondary metadata content information comprises at least objectrecognition information identified from the first video content, facialrecognition information identified from the first video content, speechrecognition identified from audio of the first video content, or textinformation identified from the first video content.
 12. The system ofclaim 9, wherein the one or more controllers are further configured tonormalize at least one of the plurality of activity components relativeto corresponding activity components of a plurality of other contentdistributed during the particular time duration.
 13. The system of claim11, wherein the secondary metadata content information further comprisespredictive information related to at least the first video content orthe particular time duration.
 14. The system of claim 12, wherein theone or more controllers are further configured to assign a performancegrade to the first video content for each of the plurality of activityfactors, wherein each performance grade corresponds to a standard scorerelative to the plurality of other content.
 15. The system of claim 12,wherein the one or more controllers are further configured to output analert related to the at least one activity component when the activitycomponent includes activity outside of a predetermined thresholdactivity range.
 16. The system of claim 14, wherein the one or morecontrollers are further configured to output data via a display ofnormalized activity components for a second video content of theplurality of other content at the first time point along with the secondcontent at the first time point.
 17. A method comprising: displaying areproduction of first video content with first activity information;displaying a reproduction of second video content with second activityinformation; wherein the first activity information corresponds to agraphical representation of audience retention during a presentation ofthe first content to a first plurality of viewers and the secondactivity information corresponds to a graphical representation ofaudience retention during presentation of the second content to a secondplurality of viewers; wherein the first activity information isgenerated by: obtaining primary metadata information related to thefirst video content, wherein the first video content is viewed by thefirst plurality of viewers during the presentation of the first videocontent; obtaining secondary metadata content information related to thefirst video content, wherein the secondary metadata content informationidentifies specific content within the first video content; obtaininghistorical audience information based on the primary metadatainformation and the secondary metadata content information, wherein thehistorical audience information indicates previous audience activityassociated with particular content identified by the primary metadatainformation or the secondary metadata content information; obtainingaudience activity information of the first plurality of viewers of thefirst video content, wherein the audience activity information relatesto audience retention during the presentation of the first videocontent; and generating the first activity information based on theaudience activity information and the historical audience information.18. The method of claim 17, wherein the reproductions of the first videocontent and the second video content are simultaneously displayed. 19.The method of claim 17, wherein the second video content corresponds tocontent different from the first video content, and the first videocontent and the second video content were respectively presented to thefirst plurality and the second plurality of viewers at the same time.